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Kento Tanaka

Publications and source records attributed to Kento Tanaka.

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SmartBAN on Silicon by Structured Behavioral Modeling

Wireless body area networks (WBANs) are a key enabling technology for the Internet of Medical Things (IoMT). SmartBAN, standardized by ETSI and later adopted as an IEC international standard, defines a lightweight WBAN protocol with time-division multiple access (TDMA)-based physical (PHY) and media access control (MAC) layers, yet no implementation on commercial hardware has been reported. The standard specifies frame formats and channel structure but leaves internal device behaviors unspecified: phase control and connection lifecycle lack transition logic, while slot-level timing and scheduling policy lack parametric guidance. This paper addresses these omissions through structured behavioral modeling and model-driven implementation. Two Mealy-type finite automata -- one for the Hub (3 states, 5 transitions), one for each Node (5 states, 8 transitions) -- capture phase control and connection lifecycle as a hardware-independent design blueprint whose transition tables map directly to firmware dispatch logic; slot-level timing and scheduling policy are resolved through realization on the nRF54L15, a commercial Arm Cortex-M33 wireless system-on-chip (SoC) running Zephyr real-time operating system (RTOS). Experiments with sixteen concurrently scheduled sensor nodes over 25 hours validate the design for the initial connection and uplink data paths: all 13 modeled transitions were exercised with sub-millisecond per-slot timing jitter ($P_{99} <$ 754 $\mu$s, slot-independent across all 16 slots), 99.99% packet delivery, and autonomous disconnection recovery. A same-SoC Bluetooth Low Energy (BLE) comparison quantifies the determinism-efficiency tradeoff: SmartBAN achieves substantially lower timing jitter at higher energy cost, the majority of which is attributable to software radio processing rather than the protocol-level duty cycle.

cs.NI

Euclidean k-center Fair Clusterings

Many approximation algorithms and heuristic algorithms to find a fair clustering have emerged. In this paper we define a new and natural variant of fair clustering problem and design a polynomial time algorithm to compute an optimal fair clustering. Let P be a set of n points on a plane, and each point has a color in C, corresponding to a group. For each color q in C, a lower bound l(q) and an upper bound u(q) are given. Then we define the fair clustering problem as follows. The fair k-clustering problem is to find a partition of P into a set of k clusters with a minimum cost such that each cluster contains at least l(q) and at most u(q) points in P with color q. By l(q) and u(q) each cluster cannot contain too few or too many points with a specific color. If we regard a color to a gender or a minority ethnic group, the clustering corresponds to a fair clustering.

cs.CG